https://github.com/khazarkhorrami/VGS_alignment
Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models
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Cross-Modal Information Retrieval (CMIR) is the task of finding relevant items across different modalities. For example, given an image, find a text or vice versa. The main challenge in CMIR is known as the heterogeneity gap: since items from different modalities have different data types, the similarity between them cannot be measured directly. Therefore, the majority of CMIR methods published to date attempt to bridge this gap by learning a latent representation space, where the similarity between items from different modalities can be measured.
Source: Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
Sketchformer: Transformer-based Representation for Sketched Structure
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
MuLan: A Joint Embedding of Music Audio and Natural Language
RadCLIP: Enhancing Radiologic Image Analysis through Contrastive Language-Image Pre-training
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
Dissecting Deep Metric Learning Losses for Image-Text Retrieval
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
VinVL: Revisiting Visual Representations in Vision-Language Models
Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Self-supervised vision-language pretraining for Medical visual question answering
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.